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AKTU Computer Vision (BCIT053) Syllabus PDF – B.Tech 3rd Year
Computer Vision BCIT053
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Official AKTU syllabus of Computer Vision (BCIT053) for B.Tech 3rd Year – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Computer Vision
Download PDFBCIT053 · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course , the student will be able to:
| CO 1 | Implement fundamental image processing techniques required for computer vision. | K , K 1 2 |
| CO 2 | Perform shape analysis and implement boundary tracking techniques. | K , K 1 2 |
| CO 3 | Apply Hough Transformation for line, circle, ellipse detections. | K , K 3 4 |
| CO 4 | Apply 3D vision and motion related techniques. | K , K 3 4 |
| CO 5 | Develop applications using Computer vision techniques. | K , K 1 2 |
Course Content
L-T-P: 3-0-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | 1. Image Processing Fundamentals: Review of image processing techniques, Classical filtering operations, Thresholding Techniques, Edge detection techniques, Corner and interest point detection, Mathematical Morphology, Texture. | 08 |
| II | 1. Shapes and Regions: Binary shape analysis, connectedness, object labeling and counting, size filtering, distance functions, Skeletons and thinning, deformable shape analysis, Boundary tracking procedure, active contours, Shape model and shape recognition, centroidal profiles, handling occlusion, boundary length measure, boundary descriptors, chain codes, Fourier descriptors, region descriptors. | 08 |
| III | 1. Hough Transform: Line detection, Hough Transform (HT) for line detection, foot of normal method, line localization, line fitting, RANSAC for straight line detection, HT based circular object detection, accurate center location, speed problem, ellipse detection, Case Studies: Human Iris location, hole detection, generalized Hough Transformation (GHT), Spatial matched filtering, GHT for ellipse detection, object location, GHT for feature collation. | 08 |
| IV | Vision and Motion: Methods for 3D vision, projection schemes, shape from shading, Photometric stereo, shape from texture, shape from focus, active range finding, surface representation, point-based representation, Volumetric representation, 3D object recognition, 3D reconstruction. Introduction to motion, triangulation, bundle adjustment, translational alignment, parametric motion, Spline-based motion, optional flow, layered motion. | 08 |
| V | Applications: Photo Album: Face detection, face recognition, Eigen faces, Active appearance and 3D shape models for face applications. Surveillance: Foreground and background separation, Particle filters, Chamfer matching, tacking and occlusion, combining view from multiple cameras, Human gait analysis. In vehicle vision system: locating roadways, road marking, identifying road signs, locating pedestrians. | 08 |
Text Books
- E. R. Davies, Computer and Machine Vision, Forth Edition, Academic Press, 2011
- R. Szeliski, Computer Vision: Algorithms and Applications, Springer, 2011
- Simon D. J. Prince, Computer Vision: Models, Learning, and Inference, Cambridge University Press, 2012.
Reference Books
- D. L. Baggio et.al., Mastering OpenCV with Practical computer vision projects, Packt publishing, 2012.
- Jan Erik Solem, Programming Computer Vision with Python: Tools and Algorithms for analysing images, O’Reilly Media, 2012
- Mark Nixon and Alberto S Aquado, Feature extraction and image processing for computer vision, Third edition, Academic Press, 2012
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